Data mining based on time series analysis can be a difficult task. Time series analysis typically performed on data to discover features in the data may include, but is not limited to, data aggregation, data clustering, and principal component analysis (PCA). In order to perform such feature discovery, the data is typically processed prior to analysis to create a substantially complete and synchronized set of data for analysis. This is because portions of time series data can be incomplete and/or out of sync with other portions of time series data being analyzed. Certain existing data pre-processing techniques can be elaborate and complex, but still not necessarily yield adequate feature discovery results.